Prediksi Jumlah Kunjungan Pasien pada Bidan Praktik Mandiri dengan Jaringan Syaraf Tiruan Backpropagation

Independent Midwives (BPM) are important in providing health services for mothers and children. One of the main challenges in managing BPM is the uncertain fluctuation in patient visits, making it difficult to plan resources, such as medical personnel, drug supplies, and other supporting facilities. If the number of patient visits cannot be predicted properly, the risk of shortages or excess resources becomes higher, which can impact operational efficiency and the quality of health services. Uncertainty in the number of patients can also affect financial planning and readiness to face a surge in visits. Based on this, this study aims to develop a prediction model for the number of patient visits using Artificial Neural Networks (ANN) with the Backpropagation method. The dataset uses data on the number of Antenatal Care (ANC) patient visits over the past three years. The results of the model evaluation were carried out based on the Mean Squared Error (MSE) value and the prediction accuracy level presented more than 94% accuracy level. The evaluation results also obtained an MSE value of 0.0023, and MAPE of 5.62% so that the results can be stated that the model prediction error is within acceptable limits. This predictive model can contribute to assisting BPM in resource planning, improving service efficiency, and strategic decision-making in managing health facilities